Application of machine learning algorithm in diabetes risk prediction of physical examination population

OUYANG Ping; LI Xiao-xi; LENG Fen; LAI Xiao-ying; ZHANG Hui-ming; YAN Chuan-jie; WANG Chu-qiong; BAI Yu; XING Zhi-qiang; LIU Xu-tao; MIAO Miao; DENG Kan; LI Wen-yuan

doi:10.16462/j.cnki.zhjbkz.2021.07.020

Volume 25 Issue 7

Aug. 2021

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(7): 849-853, 868

OUYANG Ping, LI Xiao-xi, LENG Fen, LAI Xiao-ying, ZHANG Hui-ming, YAN Chuan-jie, WANG Chu-qiong, BAI Yu, XING Zhi-qiang, LIU Xu-tao, MIAO Miao, DENG Kan, LI Wen-yuan. Application of machine learning algorithm in diabetes risk prediction of physical examination population[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 849-853, 868. doi: 10.16462/j.cnki.zhjbkz.2021.07.020

Citation:

OUYANG Ping, LI Xiao-xi, LENG Fen, LAI Xiao-ying, ZHANG Hui-ming, YAN Chuan-jie, WANG Chu-qiong, BAI Yu, XING Zhi-qiang, LIU Xu-tao, MIAO Miao, DENG Kan, LI Wen-yuan. Application of machine learning algorithm in diabetes risk prediction of physical examination population[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 849-853, 868. doi: 10.16462/j.cnki.zhjbkz.2021.07.020

Citation:

OUYANG Ping, LI Xiao-xi, LENG Fen, LAI Xiao-ying, ZHANG Hui-ming, YAN Chuan-jie, WANG Chu-qiong, BAI Yu, XING Zhi-qiang, LIU Xu-tao, MIAO Miao, DENG Kan, LI Wen-yuan. Application of machine learning algorithm in diabetes risk prediction of physical examination population[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(7): 849-853, 868. doi: 10.16462/j.cnki.zhjbkz.2021.07.020

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Application of machine learning algorithm in diabetes risk prediction of physical examination population

doi: 10.16462/j.cnki.zhjbkz.2021.07.020

1.
Department of Health Management Section, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
2.
Beijing Dudu Yida Technology Co. Ltd, Beijing 100192, China
3.
Hospital Office, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

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Corresponding author: LI Wen-yuan, E-mail: liwy666@163.com
Received Date: 2020-07-16
Rev Recd Date: 2020-12-07

Available Online: 2021-08-13

Publish Date: 2021-07-10

Abstract

Abstract

Objective To explore the predictive effect and influencing factors of Logistic regression analysis model and Light GBM algorithm on the development of diabetes in the physical examination population. Methods A total of 36 292 subjects without diabetes were selected from the Health Management Center of Nanfang Hospital from August 2003 to April 2019. We ramdomly selected 70% samples by stratification to construct trainingset. The independent variables were 34 indicators including gender, age, body mass index (BMI), waist circumference, heart rate, systolic blood pressure, diastolic blood pressure, and fasting blood glucose in the first physical examination. We defined the dependent variable as developing diabetes within 5 years from the first physical examination.Logistic regression analysis model and LightGBM (light gradient boosting machine) algorithm was uesd to establish diabetes prediction models, respectively. The prediction model was applied to the remaining 30% samples and the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the prediction effect. Results The AUC of the Logistic regression algorithm model was 0.906, while the AUC of the LightGBM analysis model was 0.910. At the optimal critical point, the sensitivity and specificity of the Logistic regression analysis model were 81.5% and 84.3%, respectively. And the sensitivity and specificity of the LightGBM analysis model were 81.6% and 85.2%, respectively. Conclusion The Logistic regression algorithm model and LightGBM algorithm model have good prediction effect on the development of diabetes in the physical examination population.
- Diabetes mellitus,
- Physical examination,
- Logistic regression analysis model,
- LightGBM model

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References(24)

References

[1]	Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045[J]. Diabetes Res Clin Pract, 2018, 138: 271-281. DOI: 10.1016/j.diabres.2018.02.023.
[2]	Wei Y, Wang J, Han X, et al. Metabolically healthy obesity increased diabetes incidence in a middle‐aged and elderly Chinese population[J]. Diabetes Metab Res Rev, 2019, 36(1). DOI: 10.1002/dmrr.3202.
[3]	Peer N, Balakrishna Y, Durao S. Screening for type 2 diabetes mellitus[J]. Cochrane Database Syst Rev, 2020, (5): 1465-1858. DOI: 10.1002/14651858.cd005266.
[4]	Ekoe J, Goldenberg R, Katz P. Screening for diabetes in adults[J]. Can J Diabetes, 2018, 42: S16-S19. DOI: 10.1016/j.jcjd.2017.10.004.
[5]	覃伟, 高敏, 沈莹, 等. 基于机器学习算法的2型糖尿病患者3个月血糖预测[J]. 中华疾病控制杂志, 2019, 23(11): 1313-1317. DOI: 10.16462/j.cnki.zhjbkz.2019.11.003. Qin W, Gao M, Shen Y, et al. Prediction of 3-mouth glycemic control in type 2 diabetes mellitus based on machine learning algorithm[J]. Chin J Dis Control Prev, 2019, 23(11): 1313-1317. DOI: 10.16462/j.cnki.zhjbkz.2019.11.003.
[6]	Mahboob AT, Iqbal MA, Ali Y, et al. A model for early prediction of diabetes[J]. Inform Med Unlocked, 2019, 16: 100204. DOI: 10.1016/j.imu.2019.100204.
[7]	Dinh A, Miertschin S, Young A, et al. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning[J]. BMC Med Inform Decis Mak, 2019, 19(1): 211. DOI: s12911-019-0918-5.
[8]	Mamuda M, Sathasivam S. Predicting the survival of diabetes using neural network[J]. AIP Conf Proc, 2017, 1870(1). DOI: 10.1063/1.4995878.
[9]	吴文军, 徐蕾, 黄丽梅, 等. 2016-2017年上海市社区糖尿病高危人群筛查结果分析[J]. 中华疾病控制杂志, 2019, 23(7): 802-806. DOI: 10.16462/j.cnki.zhjbkz.2019.07.012. Wu WJ, Xu L, Huang LM, et al. Analysis of diabetes screening in high-risk population in different communities in Shanghai from 2016 to 2017[J]. Chin J Dis Control Prev, 2019, 23(7): 802-806. DOI: 10.16462/j.cnki.zhjbkz.2019.07.012.
[10]	Lynam AL, Dennis JM, Owen KR, et al. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults[J]. Diagn Progn Res, 2020, 4(1): 6. DOI: 10.1186/s41512-020-00075-2.
[11]	Ke G, Meng Q, Finley T, et al. LightGBM: a highly efficient gradient boosting decision tree[J]. Adv Neural Inf Process Syst, 2017: 3146-3154. doi: 10.5555/3294996.3295074
[12]	中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 等. 2型糖尿病基层诊疗指南(实践版·2019)[J]. 中华全科医师杂志, 2019, 18(9): 810-818. DOI:10.3760/cma.j.issn.1671-7368. 2019.09.003. Chinese Medical Association, Chinese Medical Journals Publishing House, Chinese Society of General Practice, et al. Guideline for primary care of type 2 diabetes: practice version(2019)[J]. Chin J Gen Pract, 2019, 18(9): 810-818. DOI: 10.3760/cma.j.issn.1671-7368.2019.09.003.
[13]	Shuai, Y, Zheng Y, Huang H, et al. Hybrid Software Obsolescence Evaluation Model Based on PCA-SVM-GridSearchCV[C] // 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) IEEE. Beijing: IEEE, 2018: 449-453.
[14]	Mugeni R, Aduwo JY, Briker SM, et al. A Review of diabetes prediction equations in African descent populations[J]. Front Endocrinol (Lausanne), 2019, 10: 663. DOI: 10.3389/fendo.2019.00663.
[15]	Xie Z, Nikolayeva O, Luo J, et al. Building risk prediction models for type 2 diabetes using machine learning techniques[J]. Prev Chronic Dis, 2019, 16: E130. DOI: 10.5888/pcd16.190109.
[16]	Chen X, Wu Z, Chen Y, et al. Risk score model of type 2 diabetes prediction for rural Chinese adults: the rural deqing cohort study[J]. J Endocrinol Invest, 2017, 40(10): 1115-1123. DOI: 10.1007/s40618-017-0680-4.
[17]	Wang K, Gong M, Xie S, et al. Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents[J]. EPMA J, 2019, 10(3): 227-237. DOI: 10.1007/s13167-019-00181-2.
[18]	Yatsuya H, Li Y, Hirakawa Y, et al. A point system for predicting 10-year risk of developing type 2 diabetes mellitus in Japanese men: aichi workers' cohort study[J]. J Epidemiol, 2018, 28(8): 347-352. DOI: 10.2188/jea.je20170048.
[19]	Zhang Y, Zhou X, Wang Q, et al. Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals[J]. Medicine (Baltimore), 2017, 96(21): e6972. DOI:10.1097/md.000000000 0006972.
[20]	Maddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes[J]. Transl Res, 2017, 184: 101-107. DOI: 10.1016/j.trsl.2017.02.004.
[21]	Holst C, Becker U, Jørgensen ME, et al. Alcohol drinking patterns and risk of diabetes: a cohort study of 70, 551 men and women from the general Danish population[J]. Diabetologia, 2017, 60(10): 1941-1950. DOI: 10.1007/s00125-017-4359-3.
[22]	Sun D, Zhou T, Heianza Y, et al. Type 2 diabetes and hypertension : a study on bidirectional causality[J]. Circ Res, 2019, 124(6): 930-937. DOI: 10.1161/circresaha.118.314487.
[23]	Schnurr TM, Jakupović H, Carrasquilla GD, et al. Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: a case-cohort study[J]. Diabetologia, 2020, 63(7): 1324-1332. DOI: 10.1007/s00125-020-05140-5.
[24]	Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes[J]. BMC Med, 2017, 15(1). DOI: 10.1186/s12916-017-0901-x.